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Surfing a Web of Trust: Reputation and Reciprocity on


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Surfing a Web of Trust: Reputation and Reciprocity on

  1. 1. Surfing a web of trust: Reputation and Reciprocity on Debra Lauterbach, Hung Truong, Tanuj Shah, Lada Adamic University of Michigan School of Information
  2. 2. <ul><li>Motivation </li></ul><ul><li>Research questions </li></ul><ul><li>Findings </li></ul><ul><li>Results of predictive modeling </li></ul><ul><li>Discussion & future work </li></ul>Outline
  3. 3. <ul><li>“ Worldwide network for making connections between travelers and the local communities they visit” </li></ul><ul><li>1,300,000+ members </li></ul><ul><li>231 countries </li></ul>About CouchSurfing
  4. 4. Whose couch would you stay on?
  5. 5. <ul><li>Online->offline transactions require even more trust </li></ul><ul><li>It’s not always easy to decide who is trustworthy </li></ul><ul><ul><li>This is why having a reputation system is key </li></ul></ul>The importance of trust
  6. 6. <ul><li>Bialski & Batorski (2006) examined which factors contribute to higher trust between CouchSurfing friends </li></ul><ul><li>Molz (2007) examined the sociological meaning of reciprocity in the context of hospitality exchanges </li></ul>Related work
  7. 7. <ul><li>Reciprocity: </li></ul><ul><ul><li>Does reciprocity exist, and to what degree? </li></ul></ul><ul><li>Trust: </li></ul><ul><ul><li>Who is doing the vouching? </li></ul></ul><ul><ul><li>Who is being vouched for? </li></ul></ul><ul><ul><li>Can we predict which connections are vouched? </li></ul></ul>Our research questions
  8. 8. <ul><li>600,000+ users </li></ul><ul><ul><li>City </li></ul></ul><ul><ul><li>Country </li></ul></ul><ul><ul><li>Date they joined CouchSurfing </li></ul></ul><ul><ul><li>Number of profile views </li></ul></ul>The data
  9. 9. <ul><li>1,500,000+ friendship connections </li></ul><ul><ul><li>Friendship degree (scale of 1-7) </li></ul></ul><ul><ul><li>Whether they met in person (Y/N) </li></ul></ul><ul><ul><li>How they met (through CS, or offline) </li></ul></ul><ul><ul><li># days traveled together </li></ul></ul><ul><ul><li># days hosted </li></ul></ul><ul><ul><li># days surfed </li></ul></ul><ul><ul><li>Whether they vouched (Y/N) </li></ul></ul><ul><ul><li>Rating of their overall experience (-1 to 1 scale) </li></ul></ul><ul><ul><li>Date friendship connection was made </li></ul></ul>The data
  10. 10. <ul><li>Most users have few friends (62% have none) </li></ul><ul><li>A small number of users conduct the majority of surfing/hosting experiences </li></ul>Patterns of activity
  11. 11. <ul><li>Direct reciprocity: 12-18% of stays are directly reciprocated </li></ul>Reciprocity A B
  12. 12. <ul><li>Generalized reciprocity: large strongly connected component (1/3 of active users) </li></ul>Reciprocity
  13. 13. Worldwide reciprocity
  14. 14. <ul><li>Vouching means you believe that friend to be trustworthy </li></ul><ul><li>You can only vouch for others if you have at least 3 vouches yourself </li></ul><ul><li>Vouching forms a small “web of trust” in the network </li></ul><ul><ul><li>6.8% of users have been vouched at least once </li></ul></ul><ul><ul><li>1.8% can vouch for others </li></ul></ul>Reputation System - Vouching “ Respecting the significance of vouching is essential to the integrity of the network... It is very important that you ONLY vouch for people that you have met in person and know well enough to believe that he or she is trustworthy.”
  15. 15. <ul><li>High rate of vouching </li></ul><ul><ul><li>95% of users with > 10 friends have been vouched </li></ul></ul><ul><ul><li>15.07 vouches given out on average </li></ul></ul><ul><ul><li>25% of friendships that can be vouched are </li></ul></ul><ul><li>High rate of reciprocity </li></ul><ul><ul><li>74.6% of vouches are reciprocated </li></ul></ul>Is the vouching system being used as intended?
  16. 16. <ul><li>Tight web of trust….or vouching too freely? </li></ul><ul><li>Mutual trust….or social pressure to reciprocate? </li></ul>Why do users vouch others?
  17. 17. <ul><li>A high number of vouches are between “CouchSurfing friends” </li></ul>Who are users vouching? Friendship degree: 1= Haven’t met yet 2= Acquaintance 3= CouchSurfing friend 4= Friend 5= Good friend 6= Close friend 7= Best friend
  18. 18. <ul><li>Can we use the available friendship connection information to predict if a connection is vouched? </li></ul><ul><li>Logistic regression model (10-fold cross-validation) </li></ul><ul><li>71% accuracy in predicting whether a random edge is vouched </li></ul><ul><li>Most predictive attributes were friendship degree, rating of experience, how they met </li></ul>Predicting vouches
  19. 19. <ul><li>Two-step indirect measure for propagating vouches: </li></ul>Predicting vouches - global measures A B C D ? Indirect vouch score for A->D: = 1/n(B) + 1/n(C)
  20. 20. <ul><li>Results from logistic regression for each variable alone: </li></ul><ul><li>Global measures are poor predictors of whether an edge is vouched </li></ul>Predicting vouches - global measures Predictive accuracy: Variable 50.6% PageRank 54.2% 2-step vouch propagation 55.8% Jaccard coefficient 67.7% Friendship degree
  21. 21. <ul><li>We examined the properties of a worldwide network used to facilitate offline interactions. </li></ul><ul><li>Friendship degree information is beneficial </li></ul><ul><li>Global measures may be useful in assigning overall reputation scores, but not for predicting if a specific person will vouch for another or not </li></ul><ul><li>Further work is needed to determine if vouches are given too freely </li></ul>Summary
  22. 22. <ul><li>Questions? </li></ul><ul><li>Debra Lauterbach [email_address] </li></ul><ul><li>Hung Truong [email_address] </li></ul><ul><li>Tanuj Shah [email_address] </li></ul><ul><li>Lada Adamic [email_address] </li></ul>Thank you!